Blood pressure prediction based on multi-sensor information fusion of electrocardiogram, photoplethysmography, and pressure pulse waveform

Abstract

Cardiovascular diseases are now the leading cause of death that endangers people’s health. Thus, a precise and reliable blood pressure (BP) prediction method is essential. This paper proposes a noninvasive BP prediction method with the multi-feature fusion of electrocardiogram (ECG), photoplethysmography (PPG), and pressure pulse waveform (PPW). A multi-sensor information acquisition platform was developed to collect cardiovascular-related signals. Besides, the algorithms were designed to clean, preprocess, and extract features from sample data. Furthermore, the BP prediction model was constructed by using feature selection and feature fusion based on Random Forest Regression (RFR). Finally, the importance of features used for blood pressure prediction was analyzed, and the results of RFR-based blood pressure prediction were compared with those of other machine learning algorithms. The mean absolute errors of systolic and diastolic blood pressure prediction reached 0.90 mmHg and 2.47 mmHg, respectively. The results of the BP prediction model based on multi-sensor information fusion meet both the AAMI and BHS standards.